DAI Research Domains

Independent Research at the
Intersection of AI and Finance

DAI (DStrategyTech AI) publishes practitioner-led analysis, briefings, and benchmarks across three research domains — designed to help financial leaders, risk professionals, and technology operators make better decisions about AI.

Research Domain
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Financial Risk

AI is changing how institutions model and respond to risk — and introducing new categories of risk in the process.

Topics include

  • AI model risk and governance
  • Fraud detection and prevention
  • Deepfake financial fraud
  • Identity risk in digital banking
  • Regulatory compliance: OCC, CFPB, FFIEC, SEC
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Core Focus · Financial Operations
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Financial Operations

AI is automating financial workflows faster than most organizations are prepared for. We research what responsible adoption looks like in practice.

Topics include

  • AI-driven automation in finance functions
  • Cost management and optimization
  • AI in audit and internal controls
  • Process intelligence and workflow analysis
  • Operational risk from agentic AI in finance
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Research Domain
📈

Prediction Markets & Forecasting

Forecasting is getting faster, more granular, and increasingly AI-driven. We research the accuracy, adoption, and strategic use of these tools.

Topics include

  • AI economic forecasting models
  • Prediction market accuracy and adoption
  • Real-time risk signal analysis
  • Scenario modeling for financial institutions
  • LLM applications in financial forecasting
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Practitioner-Led. Openly Accessible. Honest About Uncertainty.

DAI was built on a simple conviction: the most important research on AI and financial services should not be gatekept. It should be grounded in real-world practice, independent of vendor relationships, and free to read. Every briefing, benchmark, and analysis we publish is evaluated against one question — does this help someone make a better decision?

Approach Practitioner-Led
Access No Paywalls
Independence Vendor-Neutral
Standard Decision-Relevant